OpenRaiser/Intern-Atlas huggingface dataset:
Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sub-Linear Storage Cost
Venue: arXiv cs.RO 2017
Year: 2017

Robotic and animal mapping systems share many challenges and characteristics: they must function in a wide variety of environmental conditions, enable the robot or animal to navigate effectively to find food or shelter, and be computationally tractable from both a speed and storage perspective. With regards to map storage, the mammalian brain appears to take a diametrically opposed approach to all current robotic mapping systems. Where robotic mapping systems attempt to solve the data association problem to minimise representational aliasing, neurons in the brain intentionally break data association by encoding large (potentially unlimited) numbers of places with a single neuron. In this paper, we propose a novel method based on supervised learning techniques that seeks out regularly repeating visual patterns in the environment with mutually complementary co-prime frequencies, and an encoding scheme that enables storage requirements to grow sub-linearly with the size of the environment being mapped. To improve robustness in challenging real-world environments while maintaining storage growth sub-linearity, we incorporate both multi-exemplar learning and data augmentation techniques. Using large benchmark robotic mapping datasets, we demonstrate the combined system achieving high-performance place recognition with sub-linear storage requirements, and characterize the performance-storage growth trade-off curve. The work serves as the first robotic mapping system with sub-linear storage scaling properties, as well as the first large-scale demonstration in real-world environments of one of the proposed memory benefits of these neurons.
On the Diversity of Realistic Image Synthesis
Venue: arXiv cs.CV 2017
Year: 2017

Many image processing tasks can be formulated as translating images between two image domains, such as colorization, super resolution and conditional image synthesis. In most of these tasks, an input image may correspond to multiple outputs. However, current existing approaches only show very minor diversity of the outputs. In this paper, we present a novel approach to synthesize diverse realistic images corresponding to a semantic layout. We introduce a diversity loss objective, which maximizes the distance between synthesized image pairs and links the input noise to the semantic segments in the synthesized images. Thus, our approach can not only produce diverse images, but also allow users to manipulate the output images by adjusting the noise manually. Experimental results show that images synthesized by our approach are significantly more diverse than that of the current existing works and equipping our diversity loss does not degrade the reality of the base networks.
Implementation of mixed-dimensional models for flow in fractured porous media
Venue: arXiv cs.CE 2017
Year: 2017

Models that involve coupled dynamics in a mixed-dimensional geometry are of increasing interest in several applications. Here, we describe the development of a simulation model for flow in fractured porous media, where the fractures and their intersections form a hierarchy of interacting subdomains. We discuss the implementation of a simulation framework, with an emphasis on reuse of existing discretization tools for mono-dimensional problems. The key ingredients are the representation of the mixed-dimensional geometry as a graph, which allows for convenient discretization and data storage, and a non-intrusive coupling of dimensions via boundary conditions and source terms. This approach is applicable for a wide class of mixed-dimensional problems. We show simulation results for a flow problem in a three-dimensional fracture geometry, applying both finite volume and virtual finite element discretizations.
Light Field Segmentation From Super-pixel Graph Representation
Venue: arXiv cs.CV 2017
Year: 2017

Efficient and accurate segmentation of light field is an important task in computer vision and graphics. The large volume of input data and the redundancy of light field make it an open challenge. In the paper, we propose a novel graph representation for interactive light field segmentation based on light field super-pixel (LFSP). The LFSP not only maintains light field redundancy, but also greatly reduces the graph size. These advantages make LFSP useful to improve segmentation efficiency. Based on LFSP graph structure, we present an efficient light field segmentation algorithm using graph-cuts. Experimental results on both synthetic and real dataset demonstrate that our method is superior to previous light field segmentation algorithms with respect to accuracy and efficiency.
On the one method of a third-degree bezier type spline curve construction
Venue: arXiv cs.GR 2017
Year: 2017

A method is proposed for constructing a spline curve of the Bezier type, which is continuous along with its first derivative by a piecewise polynomial function. Conditions for its existence and uniqueness are given. The constructed curve lies inside the convex hull of the control points, and the segments of the broken line connecting the control points are tangent to the curve. To construct the curve, we use the approach proposed earlier for constructing a parabolic spline. The idea is to use additional points with unknown values of some function. Additional points are used as spline nodes, and the function values are determined from the condition of the first derivative continuity of a piecewise polynomial curve. In multiple interpolation nodes, the function takes the given values and the values of the first derivative, which are determined by the control points. Examples of constructing a spline curve are given.
Learning a Wavelet-like Auto-Encoder to Accelerate Deep Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Accelerating deep neural networks (DNNs) has been attracting increasing attention as it can benefit a wide range of applications, e.g., enabling mobile systems with limited computing resources to own powerful visual recognition ability. A practical strategy to this goal usually relies on a two-stage process: operating on the trained DNNs (e.g., approximating the convolutional filters with tensor decomposition) and fine-tuning the amended network, leading to difficulty in balancing the trade-off between acceleration and maintaining recognition performance. In this work, aiming at a general and comprehensive way for neural network acceleration, we develop a Wavelet-like Auto-Encoder (WAE) that decomposes the original input image into two low-resolution channels (sub-images) and incorporate the WAE into the classification neural networks for joint training. The two decomposed channels, in particular, are encoded to carry the low-frequency information (e.g., image profiles) and high-frequency (e.g., image details or noises), respectively, and enable reconstructing the original input image through the decoding process. Then, we feed the low-frequency channel into a standard classification network such as VGG or ResNet and employ a very lightweight network to fuse with the high-frequency channel to obtain the classification result. Compared to existing DNN acceleration solutions, our framework has the following advantages: i) it is tolerant to any existing convolutional neural networks for classification without amending their structures; ii) the WAE provides an interpretable way to preserve the main components of the input image for classification.
Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works
Venue: arXiv cs.LG 2017
Year: 2017

With the exponential increase in the amount of digital information over the internet, online shops, online music, video and image libraries, search engines and recommendation system have become the most convenient ways to find relevant information within a short time. In the recent times, deep learning's advances have gained significant attention in the field of speech recognition, image processing and natural language processing. Meanwhile, several recent studies have shown the utility of deep learning in the area of recommendation systems and information retrieval as well. In this short review, we cover the recent advances made in the field of recommendation using various variants of deep learning technology. We organize the review in three parts: Collaborative system, Content based system and Hybrid system. The review also discusses the contribution of deep learning integrated recommendation systems into several application domains. The review concludes by discussion of the impact of deep learning in recommendation system in various domain and whether deep learning has shown any significant improvement over the conventional systems for recommendation. Finally, we also provide future directions of research which are possible based on the current state of use of deep learning in recommendation systems.
Advanced EMT and Phasor-Domain Hybrid Simulation with Simulation Mode Switching Capability for Transmission and Distribution Systems
Venue: arXiv cs.SY 2017
Year: 2017

Conventional electromagnetic transient (EMT) and phasor-domain hybrid simulation approaches presently exist for trans-mission system level studies. Their simulation efficiency is generally constrained by the EMT simulation. With an increasing number of distributed energy resources and non-conventional loads being installed in distribution systems, it is imperative to extend the hybrid simulation application to include distribution systems and integrated transmission and distribution systems. Meanwhile, it is equally important to improve the simulation efficiency as the modeling scope and complexity of the detailed system in the EMT simulation increases. To meet both requirements, this paper introduces an advanced EMT and phasor-domain hybrid simulation approach. This approach has two main features: 1) a comprehensive phasor-domain modeling framework which supports positive-sequence, three-sequence, three-phase and mixed three-sequence/three-phase representations and 2) a robust and flexible simulation mode switching scheme. The developed scheme enables simulation switching from hybrid simulation mode back to pure phasor-domain dynamic simulation mode to achieve significantly improved simulation efficiency. The proposed method has been tested on integrated transmission and distribution systems. The results show that with the developed simulation switching feature, the total computational time is significantly reduced compared to running the hybrid simulation for the whole simulation period, while maintaining good simulation accuracy.
Intel SGX Enabled Key Manager Service with OpenStack Barbican
Venue: arXiv cs.CR 2017
Year: 2017

Protecting data in the cloud continues to gain in importance, with encryption being used to achieve the desired data protection. While there is desire to use encryption, various cloud components do not want to deal with key management, which points to a strong need for a separate key management system. OpenStack Barbican is a platform developed by the OpenStack community aimed at providing cryptographic functions useful for all environments, including large ephemeral clouds. Barbican exposes REST APIs designed for the secure storage, provisioning and management of secrets such as passwords, encryption keys, and X.509 certificates, and supports plugins for a variety of crypto solutions in the backend. Crypto plugins store secrets as encrypted blobs within the Barbican database. Software based crypto plugins offer a scalable solution, but are vulnerable to system software attacks. Hardware Security Module or HSM plugins offer strong security guarantees, but they are expensive and don't scale well. We propose to build an Intel Software Guard Extension or SGX based software crypto plugin that offers security similar to an HSM with the low cost and scalability of a software based solution. We extend OpenStack Barbican API to support attestation of an Intel SGX crypto plugin, to allow clients higher confidence in the software they are using for storing keys. In addition, the API provides support for mutual attestation for Intel SGX enabled clients, multi-user key distribution, and extensions for protecting the confidentiality and integrity of the backend database.
Computing Optimal Repairs for Functional Dependencies
Venue: arXiv cs.DB 2017
Year: 2017

We investigate the complexity of computing an optimal repair of an inconsistent database, in the case where integrity constraints are Functional Dependencies (FDs). We focus on two types of repairs: an optimal subset repair (optimal S-repair) that is obtained by a minimum number of tuple deletions, and an optimal update repair (optimal U-repair) that is obtained by a minimum number of value (cell) updates. For computing an optimal S-repair, we present a polynomial-time algorithm that succeeds on certain sets of FDs and fails on others. We prove the following about the algorithm. When it succeeds, it can also incorporate weighted tuples and duplicate tuples. When it fails, the problem is NP-hard, and in fact, APX-complete (hence, cannot be approximated better than some constant). Thus, we establish a dichotomy in the complexity of computing an optimal S-repair. We present general analysis techniques for the complexity of computing an optimal U-repair, some based on the dichotomy for S-repairs. We also draw a connection to a past dichotomy in the complexity of finding a "most probable database" that satisfies a set of FDs with a single attribute on the left hand side; the case of general FDs was left open, and we show how our dichotomy provides the missing generalization and thereby settles the open problem.
Multiagent-based Participatory Urban Simulation through Inverse Reinforcement Learning
Venue: arXiv cs.MA 2017
Year: 2017

The multiagent-based participatory simulation features prominently in urban planning as the acquired model is considered as the hybrid system of the domain and the local knowledge. However, the key problem of generating realistic agents for particular social phenomena invariably remains. The existing models have attempted to dictate the factors involving human behavior, which appeared to be intractable. In this paper, Inverse Reinforcement Learning (IRL) is introduced to address this problem. IRL is developed for computational modeling of human behavior and has achieved great successes in robotics, psychology and machine learning. The possibilities presented by this new style of modeling are drawn out as conclusions, and the relative challenges with this modeling are highlighted.
Bounds on the Entropy of a Function of a Random Variable and their Applications
Venue: arXiv cs.IT 2017
Year: 2017

It is well known that the entropy $H(X)$ of a discrete random variable $X$ is always greater than or equal to the entropy $H(f(X))$ of a function $f$ of $X$, with equality if and only if $f$ is one-to-one. In this paper, we give tight bounds on $H(f(X))$ when the function $f$ is not one-to-one, and we illustrate a few scenarios where this matters. As an intermediate step towards our main result, we derive a lower bound on the entropy of a probability distribution, when only a bound on the ratio between the maximal and minimal probabilities is known. The lower bound improves on previous results in the literature, and it could find applications outside the present scenario.
Simulation of conventional cold-formed steel sections formed from Advanced High Strength Steel (AHSS)
Venue: arXiv cs.CE 2017
Year: 2017

The objective of this paper is to explore the potential impact of the use of advanced high strength steel (AHSS) to form traditional cold-formed steel structural members. In this study, shell finite element models are constructed, and geometric and material nonlinear collapse analysis performed, on simulated lipped channel cross-section cold-formed steel members roll-formed from AHSS. AHSS sheet is currently being used in automotive applications with thickness ranging from 0.35 to 0.8 mm (0.0138 to 0.0315 in.) and yield strengths from 350 to 1250 MPa (51 to 181 ksi). However, AHSS has not yet been employed in cold-formed steel construction. To assess the impact of the adoption of AHSS on cold-formed steel member strength a group of forty standard structural lipped channel cross-sections are chosen from the Steel Framing Industry Association product list and simulated with AHSS material properties. The stress-strain models used in this study are based on AHSS in production, including dual-phase and martensitic steels. The simulations consider compression with work on bending about the major axis in progress. Three different bracing conditions are employed so that the impact of local, distortional, and global buckling, including interactions can be explored. Due to the higher yield stresses of AHSS the potential for interaction and mode switching is anticipated to be greater in these members compared with conventional mild steels. The simulations provide a direct means to assess the increase in strength created by the application of AHSS, while also allowing for future exploration of the increase in buckling mode interaction, imperfection sensitivity, and strain demands inherent in the larger capacities. The work is intended to be an initial step in a longer-term effort to foster innovation in the application of new steels in cold-formed steel construction.
Long-Term Mobile Traffic Forecasting Using Deep Spatio-Temporal Neural Networks
Venue: arXiv cs.NI 2017
Year: 2017

Forecasting with high accuracy the volume of data traffic that mobile users will consume is becoming increasingly important for precision traffic engineering, demand-aware network resource allocation, as well as public transportation. Measurements collection in dense urban deployments is however complex and expensive, and the post-processing required to make predictions is highly non-trivial, given the intricate spatio-temporal variability of mobile traffic due to user mobility. To overcome these challenges, in this paper we harness the exceptional feature extraction abilities of deep learning and propose a Spatio-Temporal neural Network (STN) architecture purposely designed for precise network-wide mobile traffic forecasting. We present a mechanism that fine tunes the STN and enables its operation with only limited ground truth observations. We then introduce a Double STN technique (D-STN), which uniquely combines the STN predictions with historical statistics, thereby making faithful long-term mobile traffic projections. Experiments we conduct with real-world mobile traffic data sets, collected over 60 days in both urban and rural areas, demonstrate that the proposed (D-)STN schemes perform up to 10-hour long predictions with remarkable accuracy, irrespective of the time of day when they are triggered. Specifically, our solutions achieve up to 61% smaller prediction errors as compared to widely used forecasting approaches, while operating with up to 600 times shorter measurement intervals.
A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading
Venue: arXiv cs.LG 2017
Year: 2017

Deep neural network models have been proven to be very successful in image classification tasks, also for medical diagnosis, but their main concern is its lack of interpretability. They use to work as intuition machines with high statistical confidence but unable to give interpretable explanations about the reported results. The vast amount of parameters of these models make difficult to infer a rationale interpretation from them. In this paper we present a diabetic retinopathy interpretable classifier able to classify retine images into the different levels of disease severity and of explaining its results by assigning a score for every point in the hidden and input space, evaluating its contribution to the final classification in a linear way. The generated visual maps can be interpreted by an expert in order to compare its own knowledge with the interpretation given by the model.
Combining Static and Dynamic Features for Multivariate Sequence Classification
Venue: arXiv cs.LG 2017
Year: 2017

Model precision in a classification task is highly dependent on the feature space that is used to train the model. Moreover, whether the features are sequential or static will dictate which classification method can be applied as most of the machine learning algorithms are designed to deal with either one or another type of data. In real-life scenarios, however, it is often the case that both static and dynamic features are present, or can be extracted from the data. In this work, we demonstrate how generative models such as Hidden Markov Models (HMM) and Long Short-Term Memory (LSTM) artificial neural networks can be used to extract temporal information from the dynamic data. We explore how the extracted information can be combined with the static features in order to improve the classification performance. We evaluate the existing techniques and suggest a hybrid approach, which outperforms other methods on several public datasets.
Explicit Reference Governor for the Constrained Control of Time-Delayed Linear Systems
Venue: arXiv cs.SY 2017
Year: 2017

This paper introduces an explicit reference governor approach for controlling time delay linear systems subject to state and input constraints. The proposed framework relies on suitable invariant sets that can be built using both Lyapunov-Razumikhin and Lyapunov-Krasovskii arguments. The proposed method is validated both numerically and experimentally using several alternative formulations.
Graph Clustering with Dynamic Embedding
Venue: arXiv cs.SI 2017
Year: 2017

Graph clustering (or community detection) has long drawn enormous attention from the research on web mining and information networks. Recent literature on this topic has reached a consensus that node contents and link structures should be integrated for reliable graph clustering, especially in an unsupervised setting. However, existing methods based on shallow models often suffer from content noise and sparsity. In this work, we propose to utilize deep embedding for graph clustering, motivated by the well-recognized power of neural networks in learning intrinsic content representations. Upon that, we capture the dynamic nature of networks through the principle of influence propagation and calculate the dynamic network embedding. Network clusters are then detected based on the stable state of such an embedding. Unlike most existing embedding methods that are task-agnostic, we simultaneously solve for the underlying node representations and the optimal clustering assignments in an end-to-end manner. To provide more insight, we theoretically analyze our interpretation of network clusters and find its underlying connections with two widely applied approaches for network modeling. Extensive experimental results on six real-world datasets including both social networks and citation networks demonstrate the superiority of our proposed model over the state-of-the-art.
Grand Challenge: Optimized Stage Processing for Anomaly Detection on Numerical Data Streams
Venue: arXiv cs.PF 2017
Year: 2017

The 2017 Grand Challenge focused on the problem of automatic detection of anomalies for manufacturing equipment. This paper reports the technical details of a solution focused on particular optimizations of the processing stages. These included customized input parsing, fine tuning of a k-means clustering algorithm and probability analysis using a lazy flavor of a Markov chain. We have observed in our custom implementation that carefully tweaking these processing stages at single node level by leveraging various data stream characteristics can yield good performance results. We start the paper with several observations concerning the input data stream, following with our solution description with details on particular optimizations, and we conclude with evaluation and a discussion of obtained results.